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Efficient global optimization of a limited parameter antenna design

Efficient global optimization of a limited parameter antenna...

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conference on evolutionary and bio-inspired computation - theory and applications II

作者： O'Donnel, Terry H. Southall, Hugh L. Kaanta, Lt Bryan USAF Res Lab Hanscom AFB MA 01730 USA

ISBN: (纸本)9780819471550

Efficient Global Optimization (EGO) is a competent evolutionary algorithm suited for problems with limited design parameters and expensive cost functions [1]. Many electromagnetics problems, including some antenna designs, fall into this class, as complex electromagnetics simulations can take Substantial computational effort. This makes simple evolutionary algorithms such as genetic algorithms or particle swarms very time-consuming for design optimization, as many iterations of large populations are usually required. When physical experiments are necessary to perform tradeoffs or determine effects which may not be simulated, use of these algorithms is simply not practical at all due to the large numbers of measurements required. In this paper we first present a brief introduction to the EGO algorithm. We then present the parasitic superdirective two-element array design problem and results obtained by applying EGO to obtain the optimal element separation and operating frequency to maximize the array directivity. We compare these results to both the optimal solution and results obtained by performing a similar optimization using the Nelder-Mead downhill simplex method. Our results indicate that, unlike the Nelder-Mead algorithm, the EGO algorithm did not become stuck in local minima but rather found the area of the correct global minimum. However, Our implementation did not always drill down into the precise minimum and the addition of a local search technique seems to be indicated.

关键词： Efficient Global Optimization EGO evolutionary optimization antenna optimization DACE

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Two Satellite Image Sets for the Training and Validation of Image Processing Systems for Defense applications

Two Satellite Image Sets for the Training and Validation of ...

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conference on evolutionary and bio-inspired computation - theory and applications IV

作者： Peterson, Michael R. Aldridge, Shawn Herzog, Britny Moore, Frank Univ Hawaii Dept Comp Sci & Engn Hilo HI 96720 USA Univ Alaska Anchorage Dept Comp Sci Anchorage AK USA

ISBN: (纸本)9780819481689

Many image processing algorithms utilize the discrete wavelet transform (DWT) to provide efficient compression and near-perfect reconstruction of image data. Defense applications often require the transmission of data at high levels of compression over noisy channels. In recent years, evolutionary algorithms (EAs) have been utilized to optimize image transform filters that outperform standard wavelets for bandwidth-constrained compression of satellite images. The optimization of these filters requires the use of training images appropriately chosen for the image processing system's intended applications. This paper presents two robust sets of fifty images each intended for the training and validation of satellite and unmanned aerial vehicle (UAV) reconnaissance image processing algorithms. Each set consists of a diverse range of subjects consisting of cities, airports, military bases, and landmarks representative of the types of images that may be captured during reconnaissance missions. Optimized algorithms may be "overtrained" for a specific problem instance and thus exhibit poor performance over a general set of data. To reduce the risk of overtraining an image filter, we evaluate the suitability of each image as a training image. After evolving filters using each image, we assess the average compression performance of each filter across the entire set of images. We thus identify a small subset of images from each set that provide strong performance as training images for the image transform optimization problem. These images will also provide a suitable platform for the development of other algorithms for defense applications. The images are available upon request from the contact author.

关键词： image processing wavelets evolutionary computation unmanned aerial vehicles satellite imagery

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Locating Odour Sources with Geometric Syntactic Genetic Programming 1

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23rd European International conference on the applications of evolutionary computation (Evoapplications) Held as Part of EvoStar conference

作者： Macedo, Joao Marques, Lino Costa, Ernesto Univ Coimbra Dept Elect & Comp Engn ISR P-3030290 Coimbra Portugal Univ Coimbra Dept Informat Engn CISUC P-3030290 Coimbra Portugal

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

Using robots to locate odour sources is an interesting problem with important applications. Many researchers have drawn inspiration from nature to produce robotic methods, whilst others have attempted to automatically create search strategies with Artificial Intelligence techniques. This paper extends Geometric Syntactic Genetic Programming and applies it to automatically produce robotic controllers in the form of behaviour trees. The modification proposed enables Geometric Syntactic Genetic Programming to evolve trees containing multiple symbols per node. The behaviour trees produced by this algorithm are compared to those evolved by a standard Genetic Programming algorithm and to two bio-inspired strategies from the literature, both in simulation and in the real world. The statistically validated results show that the Geometric Syntactic Genetic Programming algorithm is able to produce behaviour trees that outperform the bio-inspired strategies, while being significantly smaller than those evolved by the standard Genetic Programming algorithm. Moreover, that reduction in size does not imply statistically significant differences in the performance of the strategies.

关键词： evolutionary Robotics Odour source localisation Genetic Programming Geometric operators

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Sensor Agnostics for Networked MAV applications

Sensor Agnostics for Networked MAV Applications

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conference on evolutionary and bio-inspired computation - theory and applications IV

作者： Mitra, Atindra K. Gates, Miguel Barber, Chris Goodwin, Thomas Selmic, Rastko Ordonez, Raul Sekman, Ali Malkani, Mohan USAF Res Lab RYRR Bldg 6202241 Avion Circle Wright Patterson AFB OH 45433 USA Louisiana Tech Univ Ruston LA 71272 USA Northrop Grumman Elect Syst Div Baltimore MD 21240 USA Univ Dayton Dayton OH 45429 USA Tennessee State Univ Nashville TN 37209 USA

ISBN: (纸本)9780819481689

A number of potential advantages associated with a new concept denoted as Sensor Agnostic Networks are discussed. For this particular paper, the primary focus is on integrated wireless networks that contain one or more MAVs (Micro Unmanned Aerial Vehicle). The development and presentation includes several approaches to analysis and design of Sensor Agnostic Networks based on the assumption of canonically structured architectures that are comprised of low-cost wireless sensor node technologies. A logical development is provided that motivates the potential adaptation of distributed low-cost sensor networks that leverage state-of-the-art wireless technologies and are specifically designed with pre-determined hooks, or facets, in-place that allow for quick and efficient sensor swaps between cost-low RF Sensors, EO Sensors, and Chem/bio Sensors. All of the sample design synthesis procedures provided within this paper conform to the structural low-cost electronic wireless network architectural constraints adopted for our new approach to generalized sensing applications via the conscious integration of Sensor Agnostic capabilities.

关键词： Wireless Sensor Network (WSN) Micro Air Vehicles (MAV) Unmanned Ground Vehicles (UGV) Layered Sensing Radio Frequency Sensor Technology Position-Adaptive Sensors

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Comparing Peircean Algorithm with Various bio-inspired Techniques for Multi-dimensional Function Optimization 42nd

Comparing Peircean Algorithm with Various Bio-inspired Techn...

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42nd SGAI International conference on Innovative Techniques and applications of Artificial Intelligence

作者： Quraishi, Quratulain Rizwan, Hammad Awais, Mian Muhammad Lahore Univ Management Sci Lahore 54792 Pakistan

ISBN: (纸本)9783031214400;9783031214417

Peirce's theory of evolution gives a highly efficient optimization algorithm in the domain of evolutionary computation (EC). Peircean evolutionary Algorithm (P-EA) has the potential to solve the existing drawbacks of classical evolutionary Algorithms such as loss of diversity, stagnation, or premature convergence. In this work, we compare P-EA with other state- of-the-art algorithms on a set of benchmark mathematical functions that are widely used to gauge their performance. These algorithms are already tested on several mathematical functions, but never have been compared with P-EA. The experimental results show that P-EA outperforms on complex functions like Michalewicz and Rastrigin with higher number of dimensions. These results help to improve the viability of P-EA for EC community.

关键词： Functional optimization evolutionary algorithms Unimodal and multimodal functions

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Evolving Point-Cloud Features For Gender Classification

Evolving Point-Cloud Features For Gender Classification

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conference on evolutionary and bio-inspired computation - theory and applications V

作者： Keen, Brittany Fouts, Aaron Rizki, Mateen Tamburino, Louis Mendoza-Schrock, Olga Wright State Univ Dept Comp Sci & Engr Dayton OH 45345 USA US Air Force Res Lab Sensor Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819486332

In this paper we explore the use of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Features are extracted from each point cloud by embedding the cloud in series of cylindrical shapes and computing a point count for each cylinder that characterizes a region of the subject. These measurements define rotationally invariant histogram features that are processed by a classifier to label the gender of each subject. Preliminary results using cylinder sizes defined by human experts demonstrate that gender can be predicted with 98% accuracy for the type of high density point cloud found in the CAESAR database. When point cloud densities are reduced to levels that might be obtained using stand-off sensors;gender classification accuracy degrades. We introduce an evolutionary algorithm to optimize the number and size of the cylinders used to define histogram features. The objective of this optimization process is to identify a set of cylindrical features that reduces the error rate when predicting gender from low density point clouds. A wrapper approach is used to interleave feature selection with classifier evaluation to train the evolutionary algorithm. Results of classification accuracy achieved using the evolved features are compared to the baseline feature set defined by human experts.

关键词： gender classification 3D point cloud feature selection evolutionary computation

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Evolving Spiking Neural Networks: A novel growth algorithm exhibits unintelligent design 2

Evolving Spiking Neural Networks: A novel growth algorithm e...

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conference on Next-Generation Robotics II and Machine Intelligence and bio-inspired computation - theory and applications IX

作者： Schaffer, J. David SUNY Binghamton Coll Community & Publ Affairs Binghamton NY 13902 USA

ISBN: (纸本)9781628416107

Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons;n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of "unintelligent design";extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.

关键词： Spiking neural networks evolutionary computation genetic algorithms topology evolution network growth process unintelligent design noise robustness tonic burster sequence detector

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applications of evolutionary computation 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： João Correia Stephen Smith Raneem Qaddoura

ISBN: (数字)9783031302299

ISBN: (纸本)9783031302282

This book constitutes the refereed proceedings of the 25th International conference on applications of evolutionary computation, Evoapplications 2023, held as part of Evo*2023, in April 2023, co-located with the Evo*2023 events EuroGP, EvoCOP, and EvoMUSART. The EuroGP focused on the technique of genetic programming, EvoCOP targeted evolutionary computation in combinatorial optimization, and EvoMUSART was dedicated to evolved and bio-inspired music, sound, art, and design.

关键词： theory of computation Computing Milieux Computer Communication Networks Special Purpose and Application-Based Systems Computer System Implementation Mathematics of Computing

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Multi-Scale Graph Theoretic Image Segmentation Using Wavelet Decomposition

Multi-Scale Graph Theoretic Image Segmentation Using Wavelet...

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conference on evolutionary and bio-inspired computation - theory and applications IV

作者： Dessauer, Michael P. Dua, Sumeet Louisiana Tech Univ Dept Comp Sci Ruston LA 71272 USA

ISBN: (纸本)9780819481689

We present a novel implementation of multi-scale graph-theoretic image segmentation using wavelet decomposition. This bottom-up segmentation through a weighted agglomeration approach utilizes the specific statistical characteristics of vehicles to quickly detect regions of interest in image frames. The method incorporates pixel intensity, texture, and boundary values to detect salient segments at multiple scales. Wavelet decomposition creates gradient and image approximations at multiple scales for fast edge weighting between nodes in the graph. Nodes with strong edge weights merge to form a single node at a higher level, where new internal statistics are calculated and edges are created with nodes at the new scale. Top-down saliency energy values are then calculated for each pixel on every scale, with the pixel labeled as a member of the node (segment) at the scale of highest energy. Salient node information is then used for binary classification as a potential object or non-object passes to classification and tracking algorithms. The method provides multi-scale segmentations by agglomerating nodes that consist of finer node agglomerations (lower scales). Criteria for weights between nodes include multi-level features, such as average intensity, variance, and boundary completion values. This method has been successfully tested on an electro-optical (EO) data set with multiple varying operating conditions (OCs). It has been shown to successfully segment both fully and partially occluded objects with minimal false alarms and false negatives. This method can easily be extended to produce more accurate segmentations through the sensor fusion of registered data types.

关键词： Graph theory target detection image segmentation wavelet decomposition multi-scale segmentation automatic target recognition

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An evolutionary algorithm technique for intelligence, surveillance, and reconnaissance plan optimization

An evolutionary algorithm technique for intelligence, survei...

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conference on evolutionary and bio-inspired computation - theory and applications II

作者： Langton, John T. Caroli, Joseph A. Rosenberg, Brad Charles River Analyt Inc 625 Mt Auburn St Cambridge MA 02138 USA Air Force Res Lab AFRL RISA Griffiss AFB NY 13441 USA

ISBN: (纸本)9780819471550

To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners Must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (12PARS) to Support Effects-based Assessment (EBA). 12PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. 12PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of 12PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the 12PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology transition

关键词： evolutionary algorithms genetic algorithms effects-based assessment (EBA) ISR planning mission rehearsal multi-objective optimization

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